AI-Driven Adaptive Route Optimization for Sustainable Urban Logistics and Supply Chain Management

Authors

  • Bharadwaj Thuraka  Department of Information Systems, Northwest Missouri State University, USA

Keywords:

AI-Driven, Adaptive Route Optimization, Sustainable, Urban Logistics, Supply Chain Management

Abstract

The increasing complexity of urban logistics, driven by rapid urbanization and surging e-commerce demand, necessitates intelligent and sustainable routing strategies. Traditional route optimization methods struggle to adapt to real-time variables such as traffic fluctuations, dynamic delivery constraints, and urban infrastructure challenges. This study explores the development and evaluation of an AI-driven adaptive routing framework that leverages real-time data, reinforcement learning, and predictive analytics to enhance last-mile delivery performance. The proposed model is formulated as a Markov Decision Process (MDP) and implemented using deep Q-learning algorithms trained on traffic and logistics datasets. Comparative analysis reveals that the AI-based approach significantly outperforms traditional methods, reducing total route distance, fuel consumption, and carbon emissions while improving delivery reliability and computational efficiency. Key sustainability metrics and scalability evaluations confirm the model’s viability for real-world deployment. The study also highlights implementation challenges such as data inconsistency, system interoperability, and the need for supportive policies. These findings underscore the transformative role of AI in advancing resilient, efficient, and environmentally sustainable urban supply chains.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-24

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

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How to Cite

[1]
Bharadwaj Thuraka, " AI-Driven Adaptive Route Optimization for Sustainable Urban Logistics and Supply Chain Management" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.667-684, May-June-2021.